Auto-calibrating Parallel MRI Reconstruction with MIMO Filters
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Solution Overview
Problem
Current MRI techniques face limitations in reducing scan time and complexity, and in producing high-quality images with lower distortion and noise, especially due to the need for calibration scans and limitations in parallel imaging methods like SENSE and GRAPPA, which result in image artifacts and increased scan duration.
Innovation Solution
The development of a novel computational method that automatically identifies a multi-input multi-output (MIMO) system of filters for interpolation, allowing for high-quality image reconstruction without a separate calibration scan, and effectively minimizes aliasing distortion, enabling faster and more robust MRI imaging with improved image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallel MRI techniques (SENSE, GRAPPA) are used to reduce scan time, then productivity is improved, but image quality deteriorates due to artifacts and distortion
Solution Approach 1:
The patent replaces traditional calibration scan procedures with an automated calibration method that uses actual imaging data to determine coil sensitivity profiles. This substitution eliminates the need for separate calibration scans and reduces manual intervention, thereby maintaining high productivity while improving image quality through more accurate sensitivity estimation.
Solution Approach 2:
The system performs self-calibration by automatically determining coil sensitivity profiles from the acquired imaging data itself, without requiring external calibration scans or manual adjustments. This self-service approach streamlines the imaging process, maintaining fast scan times while improving reconstruction accuracy and reducing artifacts.
2Measurement precision
If calibration scans are performed to improve image reconstruction accuracy, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent merges the calibration process with the actual imaging acquisition by determining coil sensitivity profiles from the same data used for image reconstruction. This consolidation eliminates separate calibration scans, thereby maintaining high measurement precision while reducing total scan time.
Solution Approach 2:
The system performs preliminary determination of coil sensitivity profiles during the imaging acquisition itself, using the acquired data to establish calibration parameters before final reconstruction. This preliminary action within the imaging process eliminates the need for separate pre-calibration scans, reducing time loss while maintaining accuracy.
3Productivity
If multiple receiver channels are used to reduce scan time, then productivity is improved, but device complexity increases
Solution Approach 1:
The system automatically determines coil sensitivity profiles from the acquired data without requiring manual calibration or complex external setup procedures. This self-service approach simplifies the operational complexity of using multiple receiver channels, allowing the system to maintain high productivity while reducing the burden of managing complex parallel imaging hardware.
Solution Approach 2:
The patent replaces complex manual calibration procedures with an automated computational approach that uses imaging data to determine sensitivity profiles. This substitution reduces the operational complexity associated with multiple receiver channels, making the system easier to use while maintaining the productivity benefits of parallel imaging.
4Loss of time
If automated calibration is implemented to reduce scan time, then loss of time is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent combines the calibration data acquisition with the actual imaging data acquisition, using the same k-space data for both purposes. This merging ensures that the coil sensitivity profiles are determined from high-quality imaging data, maintaining measurement precision while eliminating separate calibration scans and reducing time loss.
Solution Approach 2:
The system uses feedback from the acquired imaging data to iteratively refine the coil sensitivity profile estimates. This feedback mechanism ensures that even with automated calibration, the measurement precision is maintained by continuously improving the accuracy of sensitivity profiles based on the actual imaging data obtained.
Data Source
AI summary
The invention is a new computational method for the formation of magnetic resonance (MR) images. The method utilizes the data acquired by the multiple receiver channels available as parallel imaging hardware on standard MRI scanners to: (i) automatically identify a set of multi-input multi-output (MIMO) systems (e.g., MIMO filter banks) that act as interpolation kernels for acquired MR data sets (that can be subsampled with respect to the Nyquist criterion) without requiring a separate calibration scan; and (ii) use the identified MIMO systems to synthesize MR data sets that can in turn be used to produce high quality images, thereby enabling high quality imaging with fewer data samples than current methods (or equivalently provide higher image quality with the same number of data samples). A unique feature of the present invention is its ability to account for aliasing effects and minimize the associated image distortion by optimally adapting the said MIMO interpolation (image reconstruction) kernels. This ability to image with a reduced number of data samples accelerates the imaging process; hence, overcoming the main shortcoming of MRI compared to other medical imaging modalities.